The long-term objectives of the proposed research are to understand the processes and representations underlying spoken word recognition. Researchers have made significant progress in identifying the basic principles responsible for the normal listener's rapid and accurate identification of spoken words. In particular, there is now almost uniform consensus that spoken word recognition involves two fundamental processes: activation and competition. Most current models of recognition propose that stimulus input (i.e., a spoken word) activates a set of representations of similar sounding words in memory that subsequently vie for recognition. Despite the fact that similarity is afforded a crucial role in activating and discriminating among lexical competitors, we currently have little precise information regarding perceived similarity relations among spoken words. Our first goal of the project is to collect behavioral measures of perceived similarity of consonants and vowels in a variety of phonetic environments. Our second goal is to use what we learn about segmental similarity in an attempt to account for processing differences among large sets of heterogeneous spoken words. A deeper understanding of similarity-based activation and competition in spoken word perception should lead to enhanced abilities to predict difficulties in recognition that arise as a function of the listener (due to hearing loss or impaired cognitive and perceptual functioning) or the communicative situation (due to noisy or distracting environments). The proposed research should also lead to improved understanding and prediction of listeners' confusions among specific spoken words, an important goal for the speech scientist both in and out of the clinic. The theoretical aim of this proposal is to move us nearer to a computationally explicit model in which activation and competition are more closely tied to information in the signal.